On a Generalization of the Jensen–Shannon Divergence and the Jensen–Shannon Centroid

The Jensen–Shannon divergence is a renown bounded symmetrization of the Kullback–Leibler divergence which does not require probability densities to have matching supports. In this paper, we introduce a vector-skew generalization of the scalar [Formula: see text]-Jensen–Bregman divergences and derive thereof the vector-skew [Formula: see text]-Jensen–Shannon divergences. We prove that the vector-skew [Formula: see text]-Jensen–Shannon divergences are f-divergences and study the properties of these novel divergences. Finally, we report an iterative algorithm to numerically compute the Jensen–Shannon-type centroids for a set of probability densities belonging to a mixture family: This includes the case of the Jensen–Shannon centroid of a set of categorical distributions or normalized histograms.